LLM Cost Optimization Review Checklist

When to Use This Checklist

Use this checklist when an LLM application's cost is growing faster than its value. Token-based pricing means inefficient prompts, oversized models, and missing caching quietly multiply spend. This review finds savings while protecting quality, using evals to confirm that cheaper configurations still meet the bar.

How to Use This Checklist

Start by measuring spend broken down by feature, model, and tenant so you optimize where it matters. Right-size the model first, since model choice usually dominates cost, then trim prompts and enable caching for repeated requests. Cap output tokens and route simple queries to cheaper paths. Critically, re-run your eval set after each change so a cost saving never silently degrades quality, and set budget alerts to prevent runaway spend.

What Good Looks Like

A cost-optimized LLM app knows its spend by feature, model, and tenant, and uses the smallest model that meets quality on each task. Prompts are lean, repeated requests are cached, and output tokens are capped. Simple queries route to cheaper models, and per-tenant budgets enforce caps. Every cost change is validated against the eval set so quality holds, and cost and tokens per request are monitored over time.

Common Pitfalls

The biggest pitfall is cutting cost without an eval set, so quality silently drops. Using a frontier model for every task wastes money on simple queries. Verbose, redundant prompts inflate every call. Missing caching pays repeatedly for identical requests. Finally, no per-tenant budgets means a single misbehaving client can produce a surprise bill.

Related Resources

Review LLM cost optimization guidance, the fine-tuning versus RAG decision framework, LLM observability, evaluation practices, and prompt engineering best practices.